Autonomous robots are increasingly served in real-world unstructured human environments with complex long-horizon tasks, such as restaurant serving and office delivery. Task and motion planning (TAMP) is a recent research method in Artificial Intelligence Planning for these applications. TAMP integrates high-level abstract reasoning with the low-level geometric feasibility check and thus is more comprehensive than traditional task planning methods. While regular TAMP approaches are challenged by different types of uncertainties and the generalization of various applications when implemented in real-world scenarios. This article systematically reviews the most relevant approaches to TAMP and classifies them according to their features and emphasis; it categorizes the challenges and presents online TAMP and machine learning-based TAMP approaches for addressing them.

Constraints may scatter the Pareto optimal solutions of a constrained multiobjective optimization problem (CMOP) into multiple feasible regions. To avoid getting trapped in local optimal feasible regions or a part of the global optimal feasible regions, a constrained multiobjective evolutionary algorithm (CMOEA) should consider both the escape force and the expansion force carefully during the search process. However, most CMOEAs fail to provide these two forces effectively. As a remedy for this limitation, this article proposes a method called three-population evolutionary algorithm (TPEA). TPEA maintains three populations, termed Pop1, Pop2, and Pop3. Pop1 is a regular population, updated with a constrained NSGA-II variant. Pop2 and Pop3 are two auxiliary populations, containing the innermost and outermost nondominated infeasible solutions, respectively. The analysis reveals that these two types of nondominated infeasible solutions can contribute to the generation of escape and expansion forces, respectively. Due to these two forces, TPEA is likely to identify more global optimal feasible regions, which is crucial for constrained multiobjective optimization. Also, a mating selection strategy is developed in TPEA to coordinate the interaction among these three populations. Extensive experiments on 58 benchmark CMOPs and 35 real-world ones demonstrate that TPEA is significantly superior or comparable to six state-of-the-art CMOEAs on most test instances.

The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and well-distributed feasible solutions. To achieve this goal, a delicate tradeoff must be struck among feasibility, diversity, and convergence. However, balancing these three elements simultaneously through a single tradeoff model is nontrivial, mainly because the significance of each element varies in different evolutionary phases. As an alternative approach, we adapt distinct tradeoff models in various phases and introduce a novel algorithm named adaptive tradeoff model with reference points (ATM-R). In the infeasible phase, ATM-R takes the tradeoff between diversity and feasibility into account, aiming to move the population toward feasible regions from diverse search directions. In the semi-feasible phase, ATM-R promotes the transition from “the tradeoff between feasibility and diversity” to “the tradeoff between diversity and convergence.” This transition is instrumental in discovering an adequate number of feasible regions and accelerating the search for feasible Pareto optima in succession. In the feasible phase, ATM-R places an emphasis on balancing diversity and convergence to obtain a set of feasible solutions that are both well-converged and well-distributed. It is worth noting that the merits of reference points are leveraged in ATM-R to accomplish these tradeoff models. Also, in ATM-R, a multiphase mating selection strategy is developed to generate promising solutions beneficial to different evolutionary phases. Systemic experiments on a diverse set of benchmark test functions and real-world problems demonstrate that ATM-R is effective. When compared to eight state-of-the-art constrained multiobjective optimization evolutionary algorithms, ATM-R consistently demonstrates its competitive performance.

The rapid growth of vehicles as countries become more developed has brought great challenges to traffic prediction. Recent works model only local or global spatial-temporal features via graph neural networks (GNNs). Furthermore, the explicit graph structure information may contain bias, in particular, the lack of connections among multiple nodes when in fact, they are interdependent. This results in the inability to accommodate information interaction and the underutilization of high-quality information. In this paper, we design an adaptive spatial-temporal graph convolution networks (ASTGCNs) to collaboratively learn local-global spatial-temporal information for traffic prediction. Specifically, we obtain different local spatial-temporal information (i.e. spatial-temporal information of each temporal point) by dividing the global spatial-temporal information along the temporal dimension. For local spatial-temporal information, we establish an adaptive graph convolution to enhance the ability of graph convolution networks (GCNs) in managing bias in the explicit graph structure. We then employ an attention mechanism to learn the local summarization of dynamic node neighborhoods to obtain high-quality information. For global spatial-temporal information, a temporal convolution network (TCN) block and the ordinary differential equation (ODE) are utilized in our model. In essence, our proposed ASTGCNs integrates adaptive graph convolution, attention mechanism, TCN block and ODE to collaboratively learn local-global spatial-temporal information. Experimental results show that our ASTGCNs is superior to state-of-art (SOTA) methods when applied to four real-world datasets.

Multimodal Sentiment Analysis (MSA) technology, prevalent in consumer applications and mobile edge computing (MEC), enables sentiment examination through user data collected by smart devices. Despite the focus on representation learning in MSA, current methods often prioritize recognition performance through modality interaction and fusion. However, they struggle to capture multi-view sentiment cues across different interaction states, limiting multimodal sentiment representations’ expressiveness. This paper develops an innovative MSA framework, MVIR, learning multi-view interactive representations in diverse interaction states. Multilple meticulously designed sentiment tasks and an introduced self-supervised label generation algorithm (SSLGM) enable a comprehensive understanding of multi-view sentiment tendencies. The dual-view attention weighted fusion (DVAWF) module is designed to facilitate inter-modality information exchange in different interaction states. Extensive experiments on three MSA datasets affirm the efficacy and superiority of MVIR, showcasing its ability to capture sentiment information from multimodal data across various interaction states.